When sending training jobs to Vertex AI, what is commonly split into a task.py and a model.py file?

In the context of sending training jobs to Vertex AI, splitting the training code into a task.py and a model.py file is commonly done to enhance code organization and maintainability.

The task.py file typically contains the orchestration logic necessary for handling the training job. This includes defining how data is loaded, preprocessing steps, training iterations, and managing hyperparameter tuning. It serves as the entry point that Vertex AI uses to understand what steps to execute during the training process.

In contrast, the model.py file usually encapsulates the actual model definitions, including the architecture, forward pass logic, and any methods related to the model itself, such as save and load functions. By separating these concerns, developers can easily maintain and update the model architecture in one file while keeping the job management and training logic distinct in another. This separation allows for clearer code structure, aids in debugging, and helps improve collaboration among team members.

Having this separation of logic also means that teams can focus on improving either the training routines or the model design independently, which is crucial in iterative development and experimentation processes in machine learning workflows.